# What does this PR do?
We are dropping configuration via CLI flag almost entirely. If any
server configuration has to be tweak it must be done through the server
section in the run.yaml.
This is unfortunately a breaking change for whover was using:
* `--tls-*`
* `--disable_ipv6`
`--port` stays around and get a special treatment since we believe, it's
common for user dev to change port for quick experimentations.
Closes: https://github.com/meta-llama/llama-stack/issues/1076
## Test Plan
Simply do `llama stack run <config>` nothing should break :)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
- Added new Ruff lint rules to detect ambiguous or non-ASCII characters:
- Added per-file ignores where Unicode usage is still required.
- Fixed whatever had to be fixed
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
current text for 'llama stack build' and 'llama stack run' says that if
no argument is passed to '--image-name' that the active Conda
environment will be used
in reality, the active enviroment is used whether it is from conda,
virtualenv, etc.
## Test Plan
N/A
## Documentation
N/A
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
This is the second attempt to switch to system packages by default. Now
with a hack to detect conda environment - in which case conda image-type
is used.
Note: Conda will only be used when --image-name is unset *and*
CONDA_DEFAULT_ENV is set. This means that users without conda will
correctly fall back to using system packages when no --image-* arguments
are passed at all.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
Uses virtualenv:
```
$ llama stack build --template ollama --image-type venv
$ llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
[...]
Using virtual environment: /home/ec2-user/src/llama-stack/schedule/.local
[...]
```
Uses system packages (virtualenv already initialized):
```
$ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml
[...]
INFO 2025-03-27 20:46:22,882 llama_stack.cli.stack.run:142 server: No image type or image name provided. Assuming environment packages.
[...]
```
Attempt to run from environment packages without necessary packages
installed:
```
$ python -m venv barebones
$ . ./barebones/bin/activate
$ pip install -e . # to install llama command
$ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml
[...]
ModuleNotFoundError: No module named 'fastapi'
```
^ failed as expected because the environment doesn't have necessary
packages installed.
Now install some packages in the new environment:
```
$ pip install fastapi opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp aiosqlite ollama openai datasets faiss-cpu mcp autoevals
$ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml
[...]
Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)
```
Now see if setting CONDA_DEFAULT_ENV will change what happens by
default:
```
$ export CONDA_DEFAULT_ENV=base
$ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml
[...]
Using conda environment: base
Conda environment base does not exist.
[...]
```
---------
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Fixes multiple issues
1. llama stack build of dependencies was breaking with incompatible
numpy / pandas when importing datasets
Moved the notebook to start a local server instead of using library as a
client. This way the setup is cleaner since its all contained and by
using `uv run --with` we can test both the server setup process too in
CI and release time.
2. The change to [1] surfaced some other issues
- running `llama stack run` was defaulting to conda env name
- provider data was not being managed properly
- Some notebook cells (telemetry for evals) were not updated with latest
changes
Fixed all the issues and update the notebook.
### Test
1. Manually run it all in local env
2. `pytest -v -s --nbval-lax docs/getting_started.ipynb`
# What does this PR do?
A PTY is unnecessary for interactive mode since `subprocess.run()`
already inherits the calling terminal’s stdin, stdout, and stderr,
allowing natural interaction. Using a PTY can introduce unwanted side
effects like buffering issues and inconsistent signal handling. Standard
input/output is sufficient for most interactive programs.
This commit simplifies the command execution by:
1. Removing PTY-based execution in favor of direct subprocess handling
2. Consolidating command execution into a single run_command function
3. Improving error handling with specific subprocess error types
4. Adding proper type hints and documentation
5. Maintaining Ctrl+C handling for graceful interruption
## Test Plan
```
llama stack run
```
Signed-off-by: Sébastien Han <seb@redhat.com>
Reverts meta-llama/llama-stack#1252
The above PR breaks the following invocation:
```bash
llama stack run ~/.llama/distributions/together/together-run.yaml
```
# What does this PR do?
Users prefer to rely on the main CLI rather than invoking the server
through a Python module. Users interact with a high-level CLI rather
than needing to know internal module structures.
Now, when running llama stack run <path-to-config>, the server will
attempt to use the system package or a virtual environment if one is
active.
This also eliminates the current process dependency chain when running
from a virtual environment:
-> llama stack run
-> start_env.sh
-> python -m server...
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
Run:
```
ollama run llama3.2:3b-instruct-fp16 --keepalive=2m &
llama stack run ./llama_stack/templates/ollama/run.yaml --disable-ipv6
```
Notice that the server starts and shutdowns normally.
[//]: # (## Documentation)
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
This commit introduces a new logging system that allows loggers to be
assigned
a category while retaining the logger name based on the file name. The
log
format includes both the logger name and the category, producing output
like:
```
INFO 2025-03-03 21:44:11,323 llama_stack.distribution.stack:103 [core]: Tool_groups: builtin::websearch served by
tavily-search
```
Key features include:
- Category-based logging: Loggers can be assigned a category (e.g.,
"core", "server") when programming. The logger can be loaded like
this: `logger = get_logger(name=__name__, category="server")`
- Environment variable control: Log levels can be configured
per-category using the
`LLAMA_STACK_LOGGING` environment variable. For example:
`LLAMA_STACK_LOGGING="server=DEBUG;core=debug"` enables DEBUG level for
the "server"
and "core" categories.
- `LLAMA_STACK_LOGGING="all=debug"` sets DEBUG level globally for all
categories and
third-party libraries.
This provides fine-grained control over logging levels while maintaining
a clean and
informative log format.
The formatter uses the rich library which provides nice colors better
stack traces like so:
```
ERROR 2025-03-03 21:49:37,124 asyncio:1758 [uncategorized]: unhandled exception during asyncio.run() shutdown
task: <Task finished name='Task-16' coro=<handle_signal.<locals>.shutdown() done, defined at
/Users/leseb/Documents/AI/llama-stack/llama_stack/distribution/server/server.py:146>
exception=UnboundLocalError("local variable 'loop' referenced before assignment")>
╭────────────────────────────────────── Traceback (most recent call last) ───────────────────────────────────────╮
│ /Users/leseb/Documents/AI/llama-stack/llama_stack/distribution/server/server.py:178 in shutdown │
│ │
│ 175 │ │ except asyncio.CancelledError: │
│ 176 │ │ │ pass │
│ 177 │ │ finally: │
│ ❱ 178 │ │ │ loop.stop() │
│ 179 │ │
│ 180 │ loop = asyncio.get_running_loop() │
│ 181 │ loop.create_task(shutdown()) │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
UnboundLocalError: local variable 'loop' referenced before assignment
```
Co-authored-by: Ashwin Bharambe <@ashwinb>
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```
python -m llama_stack.distribution.server.server --yaml-config ./llama_stack/templates/ollama/run.yaml
INFO 2025-03-03 21:55:35,918 __main__:365 [server]: Using config file: llama_stack/templates/ollama/run.yaml
INFO 2025-03-03 21:55:35,925 __main__:378 [server]: Run configuration:
INFO 2025-03-03 21:55:35,928 __main__:380 [server]: apis:
- agents
```
[//]: # (## Documentation)
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
using `formatter_class=argparse.ArgumentDefaultsHelpFormatter` displays
(default: DEFAULT_VALUE) for each flag. add this formatter class to
build and run to show users some default values like `conda`, `8321`,
etc
## Test Plan
ran locally with following output:
before:
```
llama stack run --help
usage: llama stack run [-h] [--port PORT] [--image-name IMAGE_NAME] [--disable-ipv6] [--env KEY=VALUE] [--tls-keyfile TLS_KEYFILE] [--tls-certfile TLS_CERTFILE]
[--image-type {conda,container,venv}]
config
Start the server for a Llama Stack Distribution. You should have already built (or downloaded) and configured the distribution.
positional arguments:
config Path to config file to use for the run
options:
-h, --help show this help message and exit
--port PORT Port to run the server on. It can also be passed via the env var LLAMA_STACK_PORT. Defaults to 8321
--image-name IMAGE_NAME
Name of the image to run. Defaults to the current conda environment
--disable-ipv6 Disable IPv6 support
--env KEY=VALUE Environment variables to pass to the server in KEY=VALUE format. Can be specified multiple times.
--tls-keyfile TLS_KEYFILE
Path to TLS key file for HTTPS
--tls-certfile TLS_CERTFILE
Path to TLS certificate file for HTTPS
--image-type {conda,container,venv}
Image Type used during the build. This can be either conda or container or venv.
```
after:
```
llama stack run --help
usage: llama stack run [-h] [--port PORT] [--image-name IMAGE_NAME] [--disable-ipv6] [--env KEY=VALUE] [--tls-keyfile TLS_KEYFILE] [--tls-certfile TLS_CERTFILE]
[--image-type {conda,container,venv}]
config
Start the server for a Llama Stack Distribution. You should have already built (or downloaded) and configured the distribution.
positional arguments:
config Path to config file to use for the run
options:
-h, --help show this help message and exit
--port PORT Port to run the server on. It can also be passed via the env var LLAMA_STACK_PORT. (default: 8321)
--image-name IMAGE_NAME
Name of the image to run. Defaults to the current conda environment (default: None)
--disable-ipv6 Disable IPv6 support (default: False)
--env KEY=VALUE Environment variables to pass to the server in KEY=VALUE format. Can be specified multiple times. (default: [])
--tls-keyfile TLS_KEYFILE
Path to TLS key file for HTTPS (default: None)
--tls-certfile TLS_CERTFILE
Path to TLS certificate file for HTTPS (default: None)
--image-type {conda,container,venv}
Image Type used during the build. This can be either conda or container or venv. (default: conda)
```
[//]: # (## Documentation)
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
- From old PR, it use `BUILDS_BASE_DIR` in
`llama_stack/cli/stack/configure.py`(removed).
https://github.com/meta-llama/llama-stack/pull/371/files
- Based on the current `build` code, it should only use
`DISTRIBS_BASE_DIR` to save it.
46b0a404e8/llama_stack/cli/stack/_build.py (L298)46b0a404e8/llama_stack/cli/stack/_build.py (L301)
Pls correct me if I am understand incorrectly.
So it should no need to use in `run` now.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
It would be better to tell user env var usage in help text.
```
before:
$ llama stack run --help
--port PORT Port to run the server on. Defaults to 8321
after
$ llama stack run --help
--port PORT Port to run the server on. It can also be passed via the env var LLAMA_STACK_PORT. Defaults to 8321
```
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
I think this got accidentally removed as part of
https://github.com/meta-llama/llama-stack/pull/1250. cc @leseb
## Test Plan
After the change, this arg is no longer required.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
- Introduced logging in `StackRun` to replace print-based messages
- Improved error handling for config file loading and parsing
- Replaced `cprint` with `logger.error` for consistent error messaging
- Ensured logging is used in `server.py` for startup, shutdown, and
runtime messages
- Added missing exception handling for invalid providers
Signed-off-by: Sébastien Han <seb@redhat.com>
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
--run runs the stack that was just build using the same arguments during
the build process (image-name, type, etc)
This simplifies the workflow a lot and makes the UX better for most
local users trying to get started rather than having to match the flags
of the two commands (build and then run)
Also, moved `ImageType` to distribution.utils since there were circular
import errors with its old location
## Test Plan
tested locally using the following command:
`llama stack build --run --template ollama --image-type venv`
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
```
before:
$ llama stack run --help
usage: llama stack run [-h] [--port PORT] [--image-name IMAGE_NAME] [--disable-ipv6] [--env KEY=VALUE]
[--tls-keyfile TLS_KEYFILE] [--tls-certfile TLS_CERTFILE] [--image-type {conda,container,venv}]
config
start <<<<<<---- the server for a Llama Stack Distribution. You should have already built (or downloaded) and configured the distribution.
After:
$ llama stack run --help
usage: llama stack run [-h] [--port PORT] [--image-name IMAGE_NAME] [--disable-ipv6] [--env KEY=VALUE]
[--tls-keyfile TLS_KEYFILE] [--tls-certfile TLS_CERTFILE] [--image-type {conda,container,venv}]
config
Start <<<<<<---- the server for a Llama Stack Distribution. You should have already built (or downloaded) and configured the distribution.
```
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
add --image-type to `llama stack run`. Which takes conda, container or
venv also add start_venv.sh which start the stack using a venv
resolves#1007
## Test Plan
running locally:
`llama stack build --template ollama --image-type venv`
`llama stack run --image-type venv
~/.llama/distributions/ollama/ollama-run.yaml`
...
```
llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
Using run configuration: /Users/charliedoern/.llama/distributions/ollama/ollama-run.yaml
+ python -m llama_stack.distribution.server.server --yaml-config /Users/charliedoern/.llama/distributions/ollama/ollama-run.yaml --port 8321
Using config file: /Users/charliedoern/.llama/distributions/ollama/ollama-run.yaml
Run configuration:
apis:
- agents
- datasetio
...
```
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
Enables HTTPS option for Llama Stack.
While doing so, introduces a `ServerConfig` sub-structure to house all
server related configuration (port, ssl, etc.)
Also simplified the `start_container.sh` entrypoint to simply be
`python` instead of a complex bash command line.
## Test Plan
Conda:
Run:
```bash
$ llama stack build --template together
$ llama stack run --port 8322 # ensure server starts
$ llama-stack-client configure --endpoint http://localhost:8322
$ llama-stack-client models list
```
Create a self-signed SSL key / cert pair. Then, using a local checkout
of `llama-stack-client-python`, change
https://github.com/meta-llama/llama-stack-client-python/blob/main/src/llama_stack_client/_base_client.py#L759
to add `kwargs.setdefault("verify", False)` so SSL verification is
disabled. Then:
```bash
$ llama stack run --port 8322 --tls-keyfile <KEYFILE> --tls-certfile <CERTFILE>
$ llama-stack-client configure --endpoint https://localhost:8322 # notice the `https`
$ llama-stack-client models list
```
Also tested with containers (but of course one needs to make sure the
cert and key files are appropriately provided to the container.)
Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
Fixes: #902
For the test verified that llama stack can run if built:
* With default "base" conda environment
* With new custom conda environment using `--image-name XXX` option
In both cases llama stack starts fine (was failing with "base") before
this patch.
CC: @ashwinb
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
# What does this PR do?
Add win platform run command for stack
- [x] Addresses issue (#issue)
## Test Plan
Please describe:
- tests you ran to verify your changes with result summaries.
- provide instructions so it can be reproduced.
## Sources
Please link relevant resources if necessary.
https://github.com/meta-llama/llama-stack/pull/889
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
It's a more generic term and applicable to alternatives of Docker, such
as Podman or other OCI-compliant technologies.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
## What does this PR do?
So far `llama stack build` has always created a separate conda
environment for packaging the dependencies of a distribution. The main
reason to do so is isolation -- distributions are composed of providers
which can have a variety of potentially conflicting dependencies. That
said, this has created significant annoyance for new users since it is
not at all transparent. The fact that `llama stack run` is actually
running the code in some other conda is very surprising.
This PR tries to make things better.
- Both `llama stack build` and `llama stack run` now accept an
`--image-name` argument which represents the (conda, docker, virtualenv)
image you want to operate upon.
- For the default (conda) mode, the script checks if a current conda
environment exists. If one exists, it uses it.
- If `--image-name` is provided, that option is used. In this case, an
environment is created if needed.
- There is no automatic `llamastack-` prefixing of the environment names
done anymore.
## Test Plan
Start in a conda environment, run `llama stack build --template
fireworks`; verify that it successfully built into the current
environment and stored the build file at
`$CONDA_PREFIX/llamastack-build.yaml`. Run `llama stack run fireworks`
which started correctly in the current environment.
Ran the same build command outside of conda. It failed asking for
`--image-name`. Ran it with `llama stack build --template fireworks
--image-name foo`. This successfully created a conda environment called
`foo` and installed deps. Ran `llama stack run fireworks` outside conda
which failed. Activated a different conda, ran again, it failed saying
it did not find the `llamastack-build.yaml` file. Then used
`--image-name foo` option and it ran successfully.
# What does this PR do?
Rename environment var for consistency
## Test Plan
No regressions
## Sources
## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Ran pre-commit to handle lint / formatting issues.
- [X] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [X] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.com>
This was missed in https://github.com/meta-llama/llama-stack/pull/706. I
tested `llama_stack.distribution.server.server` but didn't test `llama
stack run`. cc @ashwinb
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
When running with dockers, the idea is that users be able to work purely
with the `llama stack` CLI. They should not need to know about the
existence of any YAMLs unless they need to. This PR enables it.
The docker command now doesn't need to volume mount a yaml and can
simply be:
```bash
docker run -v ~/.llama/:/root/.llama \
--env A=a --env B=b
```
## Test Plan
Check with conda first (no regressions):
```bash
LLAMA_STACK_DIR=. llama stack build --template ollama
llama stack run ollama --port 5001
# server starts up correctly
```
Check with docker
```bash
# build the docker
LLAMA_STACK_DIR=. llama stack build --template ollama --image-type docker
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
docker run -it -p 5001:5001 \
-v ~/.llama:/root/.llama \
-v $PWD:/app/llama-stack-source \
localhost/distribution-ollama:dev \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
Note that volume mounting to `/app/llama-stack-source` is only needed
because we built the docker with uncommitted source code.
# What does this PR do?
Automatically generates
- build.yaml
- run.yaml
- run-with-safety.yaml
- parts of markdown docs
for the distributions.
## Test Plan
At this point, this only updates the YAMLs and the docs. Some testing
(especially with ollama and vllm) has been performed but needs to be
much more tested.
**Summary:**
Extend the shorthand run command so it can run successfully when config
exists under DISTRIBS_BASE_DIR (i.e. ~/.llama/distributions).
For example, imagine you created a new stack using the `llama stack
build` command where you named it "my-awesome-llama-stack".
```
$ llama stack build
> Enter a name for your Llama Stack (e.g. my-local-stack): my-awesome-llama-stack
```
To run the stack you created you will have to use long config path:
```
llama stack run ~/.llama/distributions/llamastack-my-awesome-llama-stack/my-awesome-llama-stack-run.yaml
```
With this change, you can start it using the stack name instead of full
path:
```
llama stack run my-awesome-llama-stack
```
**Test Plan:**
Verify command fails when stack doesn't exist
```
python3 -m llama_stack.cli.llama stack run my-test-stack
```
Output [FAILURE]
```
usage: llama stack run [-h] [--port PORT] [--disable-ipv6] config
llama stack run: error: File /Users/vladimirivic/.llama/distributions/llamastack-my-test-stack/my-test-stack-run.yaml does not exist. Please run `llama stack build` to generate (and optionally edit) a run.yaml file
```
Create a new stack using `llama stack build`.
Name it `my-test-stack`.
Verify command runs successfully
```
python3 -m llama_stack.cli.llama stack run my-test-stack
```
Output [SUCCESS]
```
Listening on ['::', '0.0.0.0']:5000
INFO: Started server process [80146]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit)
```
This PR makes several core changes to the developer experience surrounding Llama Stack.
Background: PR #92 introduced the notion of "routing" to the Llama Stack. It introduces three object types: (1) models, (2) shields and (3) memory banks. Each of these objects can be associated with a distinct provider. So you can get model A to be inferenced locally while model B, C can be inference remotely (e.g.)
However, this had a few drawbacks:
you could not address the provider instances -- i.e., if you configured "meta-reference" with a given model, you could not assign an identifier to this instance which you could re-use later.
the above meant that you could not register a "routing_key" (e.g. model) dynamically and say "please use this existing provider I have already configured" for a new model.
the terms "routing_table" and "routing_key" were exposed directly to the user. in my view, this is way too much overhead for a new user (which almost everyone is.) people come to the stack wanting to do ML and encounter a completely unexpected term.
What this PR does: This PR structures the run config with only a single prominent key:
- providers
Providers are instances of configured provider types. Here's an example which shows two instances of the remote::tgi provider which are serving two different models.
providers:
inference:
- provider_id: foo
provider_type: remote::tgi
config: { ... }
- provider_id: bar
provider_type: remote::tgi
config: { ... }
Secondly, the PR adds dynamic registration of { models | shields | memory_banks } to the API surface. The distribution still acts like a "routing table" (as previously) except that it asks the backing providers for a listing of these objects. For example it asks a TGI or Ollama inference adapter what models it is serving. Only the models that are being actually served can be requested by the user for inference. Otherwise, the Stack server will throw an error.
When dynamically registering these objects, you can use the provider IDs shown above. Info about providers can be obtained using the Api.inspect set of endpoints (/providers, /routes, etc.)
The above examples shows the correspondence between inference providers and models registry items. Things work similarly for the safety <=> shields and memory <=> memory_banks pairs.
Registry: This PR also makes it so that Providers need to implement additional methods for registering and listing objects. For example, each Inference provider is now expected to implement the ModelsProtocolPrivate protocol (naming is not great!) which consists of two methods
register_model
list_models
The goal is to inform the provider that a certain model needs to be supported so the provider can make any relevant backend changes if needed (or throw an error if the model cannot be supported.)
There are many other cleanups included some of which are detailed in a follow-up comment.
* add back wizard for build
* conda build path move
* polish message
* run with name only
* prompt for build
* improve comments
* update msgs
* add new lines
* move build.yaml
* address comments
* validator for providers
* move imports
* Please enter -> enter
* comments, get started guide
* nits
* fix cprint import
* fix imports
* API Keys passed from Client instead of distro configuration
* delete distribution registry
* Rename the "package" word away
* Introduce a "Router" layer for providers
Some providers need to be factorized and considered as thin routing
layers on top of other providers. Consider two examples:
- The inference API should be a routing layer over inference providers,
routed using the "model" key
- The memory banks API is another instance where various memory bank
types will be provided by independent providers (e.g., a vector store
is served by Chroma while a keyvalue memory can be served by Redis or
PGVector)
This commit introduces a generalized routing layer for this purpose.
* update `apis_to_serve`
* llama_toolchain -> llama_stack
* Codemod from llama_toolchain -> llama_stack
- added providers/registry
- cleaned up api/ subdirectories and moved impls away
- restructured api/api.py
- from llama_stack.apis.<api> import foo should work now
- update imports to do llama_stack.apis.<api>
- update many other imports
- added __init__, fixed some registry imports
- updated registry imports
- create_agentic_system -> create_agent
- AgenticSystem -> Agent
* Moved some stuff out of common/; re-generated OpenAPI spec
* llama-toolchain -> llama-stack (hyphens)
* add control plane API
* add redis adapter + sqlite provider
* move core -> distribution
* Some more toolchain -> stack changes
* small naming shenanigans
* Removing custom tool and agent utilities and moving them client side
* Move control plane to distribution server for now
* Remove control plane from API list
* no codeshield dependency randomly plzzzzz
* Add "fire" as a dependency
* add back event loggers
* stack configure fixes
* use brave instead of bing in the example client
* add init file so it gets packaged
* add init files so it gets packaged
* Update MANIFEST
* bug fix
---------
Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Xi Yan <xiyan@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
2024-09-17 19:51:35 -07:00
Renamed from llama_toolchain/cli/stack/run.py (Browse further)
